Computer vision is an interdisciplinary field that deals with how computers can gain high-level understanding from digital images or videos. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information. From the perspective of engineering, computer vision techniques seek to automate tasks that the human visual system can do, such as pattern recognition for recognizing patterns and regularities in data, and gesture recognition for interpreting human gestures via mathematical algorithms. A variety of information, such as video, audio, still images, etc., is captured as input for pattern and/or gesture recognition applications. Due to the large amount of input data for such systems, efficient data and workload management often becomes a key for ensuring satisfactory performance, particularly for applications that require real-time feedback and/or responses.
One specification application of automated pattern and gesture recognition is sign language translation. A sign language (also known as a signed language) is a language that uses manual communication to convey meaning, ideas and thoughts, which simultaneously employs hand gestures, movement, orientation of the fingers, arms or body, and facial expressions to convey a speaker's ideas. Data and workload management techniques enable more efficient utilization of computational resources and operations of various components within a sign language translation system, thereby facilitating automated translation of sign languages in real time.